Overview

Dataset statistics

Number of variables19
Number of observations21454
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 MiB
Average record size in memory152.0 B

Variable types

Categorical3
Numeric16

Alerts

ID has a high cardinality: 21454 distinct valuesHigh cardinality
year is highly overall correlated with countryHigh correlation
ghsl_water_surface is highly overall correlated with ghsl_not_built_up and 2 other fieldsHigh correlation
ghsl_built_pre_1975 is highly overall correlated with ghsl_built_1975_to_1990 and 7 other fieldsHigh correlation
ghsl_built_1975_to_1990 is highly overall correlated with ghsl_built_pre_1975 and 7 other fieldsHigh correlation
ghsl_built_1990_to_2000 is highly overall correlated with ghsl_built_pre_1975 and 7 other fieldsHigh correlation
ghsl_built_2000_to_2014 is highly overall correlated with ghsl_built_pre_1975 and 7 other fieldsHigh correlation
ghsl_not_built_up is highly overall correlated with ghsl_water_surface and 9 other fieldsHigh correlation
ghsl_pop_density is highly overall correlated with ghsl_built_pre_1975 and 8 other fieldsHigh correlation
landcover_urban_fraction is highly overall correlated with ghsl_built_pre_1975 and 8 other fieldsHigh correlation
landcover_water_permanent_10km_fraction is highly overall correlated with ghsl_water_surface and 1 other fieldsHigh correlation
landcover_water_seasonal_10km_fraction is highly overall correlated with ghsl_water_surface and 1 other fieldsHigh correlation
nighttime_lights is highly overall correlated with ghsl_built_pre_1975 and 7 other fieldsHigh correlation
Target is highly overall correlated with ghsl_built_pre_1975 and 8 other fieldsHigh correlation
country is highly overall correlated with yearHigh correlation
urban_or_rural is highly overall correlated with ghsl_not_built_up and 3 other fieldsHigh correlation
ID is uniformly distributedUniform
ID has unique valuesUnique
Target has unique valuesUnique
ghsl_water_surface has 17033 (79.4%) zerosZeros
ghsl_built_pre_1975 has 7983 (37.2%) zerosZeros
ghsl_built_1975_to_1990 has 5604 (26.1%) zerosZeros
ghsl_built_1990_to_2000 has 4188 (19.5%) zerosZeros
ghsl_built_2000_to_2014 has 3237 (15.1%) zerosZeros
ghsl_pop_density has 1397 (6.5%) zerosZeros
landcover_urban_fraction has 414 (1.9%) zerosZeros
landcover_water_permanent_10km_fraction has 10142 (47.3%) zerosZeros
landcover_water_seasonal_10km_fraction has 3588 (16.7%) zerosZeros
nighttime_lights has 10074 (47.0%) zerosZeros

Reproduction

Analysis started2023-01-14 18:34:11.966903
Analysis finished2023-01-14 18:35:59.817935
Duration1 minute and 47.85 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

ID
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct21454
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size167.7 KiB
ID_AAIethGy
 
1
ID_iilpIHmh
 
1
ID_iiLFzlhV
 
1
ID_iiFgOwAJ
 
1
ID_ihzQMtNv
 
1
Other values (21449)
21449 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters235994
Distinct characters53
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21454 ?
Unique (%)100.0%

Sample

1st rowID_AAIethGy
2nd rowID_AAYiaCeL
3rd rowID_AAdurmKj
4th rowID_AAgNHles
5th rowID_AAishfND

Common Values

ValueCountFrequency (%)
ID_AAIethGy 1
 
< 0.1%
ID_iilpIHmh 1
 
< 0.1%
ID_iiLFzlhV 1
 
< 0.1%
ID_iiFgOwAJ 1
 
< 0.1%
ID_ihzQMtNv 1
 
< 0.1%
ID_ihvgRPzg 1
 
< 0.1%
ID_ihpxSqWR 1
 
< 0.1%
ID_ihnucldf 1
 
< 0.1%
ID_ihWRXXBP 1
 
< 0.1%
ID_ihTgrNHF 1
 
< 0.1%
Other values (21444) 21444
> 99.9%

Length

2023-01-14T21:36:00.037645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
id_aaiethgy 1
 
< 0.1%
id_aadurmkj 1
 
< 0.1%
id_aaishfnd 1
 
< 0.1%
id_aanetgmr 1
 
< 0.1%
id_abooaqli 1
 
< 0.1%
id_abrveetg 1
 
< 0.1%
id_abbcebbl 1
 
< 0.1%
id_abcnwkmc 1
 
< 0.1%
id_abmhngqz 1
 
< 0.1%
id_abqywdgi 1
 
< 0.1%
Other values (21444) 21444
> 99.9%

Most occurring characters

ValueCountFrequency (%)
D 24805
 
10.5%
I 24749
 
10.5%
_ 21454
 
9.1%
c 3422
 
1.5%
X 3421
 
1.4%
g 3406
 
1.4%
B 3385
 
1.4%
G 3383
 
1.4%
F 3377
 
1.4%
l 3370
 
1.4%
Other values (43) 141222
59.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 128836
54.6%
Lowercase Letter 85704
36.3%
Connector Punctuation 21454
 
9.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 24805
19.3%
I 24749
19.2%
X 3421
 
2.7%
B 3385
 
2.6%
G 3383
 
2.6%
F 3377
 
2.6%
W 3352
 
2.6%
T 3345
 
2.6%
J 3338
 
2.6%
N 3331
 
2.6%
Other values (16) 52350
40.6%
Lowercase Letter
ValueCountFrequency (%)
c 3422
 
4.0%
g 3406
 
4.0%
l 3370
 
3.9%
w 3362
 
3.9%
k 3344
 
3.9%
z 3330
 
3.9%
p 3325
 
3.9%
u 3324
 
3.9%
o 3320
 
3.9%
s 3320
 
3.9%
Other values (16) 52181
60.9%
Connector Punctuation
ValueCountFrequency (%)
_ 21454
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 214540
90.9%
Common 21454
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 24805
 
11.6%
I 24749
 
11.5%
c 3422
 
1.6%
X 3421
 
1.6%
g 3406
 
1.6%
B 3385
 
1.6%
G 3383
 
1.6%
F 3377
 
1.6%
l 3370
 
1.6%
w 3362
 
1.6%
Other values (42) 137860
64.3%
Common
ValueCountFrequency (%)
_ 21454
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 235994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 24805
 
10.5%
I 24749
 
10.5%
_ 21454
 
9.1%
c 3422
 
1.5%
X 3421
 
1.4%
g 3406
 
1.4%
B 3385
 
1.4%
G 3383
 
1.4%
F 3377
 
1.4%
l 3370
 
1.4%
Other values (43) 141222
59.8%

country
Categorical

Distinct18
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size167.7 KiB
Nigeria
2695 
Kenya
2626 
Tanzania
2450 
Malawi
1957 
Ethiopia
1721 
Other values (13)
10005 

Length

Max length24
Median length12
Mean length7.1224946
Min length4

Characters and Unicode

Total characters152806
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEthiopia
2nd rowEthiopia
3rd rowMozambique
4th rowMalawi
5th rowGuinea

Common Values

ValueCountFrequency (%)
Nigeria 2695
12.6%
Kenya 2626
12.2%
Tanzania 2450
11.4%
Malawi 1957
9.1%
Ethiopia 1721
 
8.0%
Ghana 1419
 
6.6%
Mali 1295
 
6.0%
Cameroon 1041
 
4.9%
Rwanda 984
 
4.6%
Senegal 903
 
4.2%
Other values (8) 4363
20.3%

Length

2023-01-14T21:36:00.295486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nigeria 2695
11.6%
kenya 2626
11.3%
tanzania 2450
10.6%
malawi 1957
 
8.4%
ethiopia 1721
 
7.4%
ghana 1419
 
6.1%
mali 1295
 
5.6%
cameroon 1041
 
4.5%
rwanda 984
 
4.2%
senegal 903
 
3.9%
Other values (12) 6091
26.3%

Most occurring characters

ValueCountFrequency (%)
a 29610
19.4%
i 18004
 
11.8%
e 14213
 
9.3%
n 13983
 
9.2%
o 8677
 
5.7%
r 6249
 
4.1%
l 4887
 
3.2%
M 4131
 
2.7%
h 3934
 
2.6%
g 3928
 
2.6%
Other values (27) 45190
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 127415
83.4%
Uppercase Letter 23182
 
15.2%
Space Separator 1728
 
1.1%
Other Punctuation 481
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 29610
23.2%
i 18004
14.1%
e 14213
11.2%
n 13983
11.0%
o 8677
 
6.8%
r 6249
 
4.9%
l 4887
 
3.8%
h 3934
 
3.1%
g 3928
 
3.1%
z 3599
 
2.8%
Other values (13) 20331
16.0%
Uppercase Letter
ValueCountFrequency (%)
M 4131
17.8%
T 2780
12.0%
N 2695
11.6%
K 2626
11.3%
G 2012
8.7%
S 1958
8.4%
C 1753
7.6%
E 1721
7.4%
L 1579
 
6.8%
R 1215
 
5.2%
Other values (2) 712
 
3.1%
Space Separator
ValueCountFrequency (%)
1728
100.0%
Other Punctuation
ValueCountFrequency (%)
' 481
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 150597
98.6%
Common 2209
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 29610
19.7%
i 18004
12.0%
e 14213
 
9.4%
n 13983
 
9.3%
o 8677
 
5.8%
r 6249
 
4.1%
l 4887
 
3.2%
M 4131
 
2.7%
h 3934
 
2.6%
g 3928
 
2.6%
Other values (25) 42981
28.5%
Common
ValueCountFrequency (%)
1728
78.2%
' 481
 
21.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 152806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 29610
19.4%
i 18004
 
11.8%
e 14213
 
9.3%
n 13983
 
9.2%
o 8677
 
5.7%
r 6249
 
4.1%
l 4887
 
3.2%
M 4131
 
2.7%
h 3934
 
2.6%
g 3928
 
2.6%
Other values (27) 45190
29.6%

year
Real number (ℝ)

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.0609
Minimum1994
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size167.7 KiB
2023-01-14T21:36:00.510353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1994
5-th percentile1999
Q12008
median2011
Q32014
95-th percentile2016
Maximum2016
Range22
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.7995794
Coefficient of variation (CV)0.0023877781
Kurtosis1.2504404
Mean2010.0609
Median Absolute Deviation (MAD)3
Skewness-1.2081735
Sum43123846
Variance23.035962
MonotonicityNot monotonic
2023-01-14T21:36:00.702235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2014 2931
13.7%
2011 2309
10.8%
2008 2196
10.2%
2015 2103
9.8%
2010 1969
9.2%
2013 1728
8.1%
2012 1587
7.4%
2016 1222
 
5.7%
2009 988
 
4.6%
2003 759
 
3.5%
Other values (9) 3662
17.1%
ValueCountFrequency (%)
1994 141
 
0.7%
1995 188
 
0.9%
1996 202
 
0.9%
1998 298
 
1.4%
1999 535
2.5%
2003 759
3.5%
2004 696
3.2%
2005 641
3.0%
2006 610
2.8%
2007 351
1.6%
ValueCountFrequency (%)
2016 1222
5.7%
2015 2103
9.8%
2014 2931
13.7%
2013 1728
8.1%
2012 1587
7.4%
2011 2309
10.8%
2010 1969
9.2%
2009 988
 
4.6%
2008 2196
10.2%
2007 351
 
1.6%

urban_or_rural
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size167.7 KiB
R
14061 
U
7393 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21454
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowR
2nd rowR
3rd rowR
4th rowR
5th rowU

Common Values

ValueCountFrequency (%)
R 14061
65.5%
U 7393
34.5%

Length

2023-01-14T21:36:00.878127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-14T21:36:01.215173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
r 14061
65.5%
u 7393
34.5%

Most occurring characters

ValueCountFrequency (%)
R 14061
65.5%
U 7393
34.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 21454
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 14061
65.5%
U 7393
34.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 21454
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 14061
65.5%
U 7393
34.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21454
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 14061
65.5%
U 7393
34.5%

ghsl_water_surface
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4353
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.028258802
Minimum0
Maximum0.96995614
Zeros17033
Zeros (%)79.4%
Negative0
Negative (%)0.0%
Memory size167.7 KiB
2023-01-14T21:36:01.409055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.23379
Maximum0.96995614
Range0.96995614
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.097490496
Coefficient of variation (CV)3.4499161
Kurtosis22.608136
Mean0.028258802
Median Absolute Deviation (MAD)0
Skewness4.4620546
Sum606.26434
Variance0.0095043968
MonotonicityNot monotonic
2023-01-14T21:36:01.664896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17033
79.4%
0.09061779622 45
 
0.2%
0.0984636738 6
 
< 0.1%
0.01093372421 5
 
< 0.1%
0.01995908722 5
 
< 0.1%
0.1340284545 5
 
< 0.1%
0.03714490154 3
 
< 0.1%
0.0004804167148 3
 
< 0.1%
0.1639126188 2
 
< 0.1%
0.0004319282544 2
 
< 0.1%
Other values (4343) 4345
 
20.3%
ValueCountFrequency (%)
0 17033
79.4%
6.913405176 × 10-81
 
< 0.1%
7.208583462 × 10-81
 
< 0.1%
2.918160552 × 10-71
 
< 0.1%
7.320875205 × 10-71
 
< 0.1%
9.822229572 × 10-71
 
< 0.1%
2.280520165 × 10-61
 
< 0.1%
2.565648908 × 10-61
 
< 0.1%
3.285057347 × 10-61
 
< 0.1%
3.469314655 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.9699561356 1
< 0.1%
0.9605258116 1
< 0.1%
0.9471977118 1
< 0.1%
0.9471973584 1
< 0.1%
0.9471962126 1
< 0.1%
0.9471961791 1
< 0.1%
0.9471953941 1
< 0.1%
0.9471939616 1
< 0.1%
0.927965564 1
< 0.1%
0.9127792162 1
< 0.1%

ghsl_built_pre_1975
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13305
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.038222426
Minimum0
Maximum0.87711581
Zeros7983
Zeros (%)37.2%
Negative0
Negative (%)0.0%
Memory size167.7 KiB
2023-01-14T21:36:01.923756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.00019752036
Q30.0079865968
95-th percentile0.25583431
Maximum0.87711581
Range0.87711581
Interquartile range (IQR)0.0079865968

Descriptive statistics

Standard deviation0.113562
Coefficient of variation (CV)2.9710831
Kurtosis17.628516
Mean0.038222426
Median Absolute Deviation (MAD)0.00019752036
Skewness4.0600065
Sum820.02392
Variance0.012896329
MonotonicityNot monotonic
2023-01-14T21:36:02.186594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7983
37.2%
0.3018118024 45
 
0.2%
0.02087912288 10
 
< 0.1%
0.02373294331 9
 
< 0.1%
0.04067659073 7
 
< 0.1%
0.05070201558 7
 
< 0.1%
0.07381085682 7
 
< 0.1%
9.251572459 × 10-57
 
< 0.1%
0.01635782602 7
 
< 0.1%
0.002180839028 6
 
< 0.1%
Other values (13295) 13366
62.3%
ValueCountFrequency (%)
0 7983
37.2%
6.917283538 × 10-81
 
< 0.1%
1.442703846 × 10-71
 
< 0.1%
1.465081253 × 10-71
 
< 0.1%
8.83797365 × 10-71
 
< 0.1%
8.909530291 × 10-71
 
< 0.1%
1.103444785 × 10-61
 
< 0.1%
1.253662959 × 10-61
 
< 0.1%
1.44936663 × 10-61
 
< 0.1%
1.50932873 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.877115813 1
< 0.1%
0.8738062489 1
< 0.1%
0.8734315531 1
< 0.1%
0.8733139612 1
< 0.1%
0.8647089728 1
< 0.1%
0.8642278604 1
< 0.1%
0.8587128693 1
< 0.1%
0.8573238101 1
< 0.1%
0.8411980888 1
< 0.1%
0.8358882073 1
< 0.1%

ghsl_built_1975_to_1990
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15737
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02864367
Minimum0
Maximum0.68501033
Zeros5604
Zeros (%)26.1%
Negative0
Negative (%)0.0%
Memory size167.7 KiB
2023-01-14T21:36:02.583352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.00070918812
Q30.00986816
95-th percentile0.20158847
Maximum0.68501033
Range0.68501033
Interquartile range (IQR)0.00986816

Descriptive statistics

Standard deviation0.077367473
Coefficient of variation (CV)2.7010321
Kurtosis16.12044
Mean0.02864367
Median Absolute Deviation (MAD)0.00070918812
Skewness3.8299052
Sum614.5213
Variance0.0059857259
MonotonicityNot monotonic
2023-01-14T21:36:02.889163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5604
 
26.1%
0.0009110213902 10
 
< 0.1%
0.001965384368 9
 
< 0.1%
0.0002590440289 7
 
< 0.1%
0.01219179209 7
 
< 0.1%
0.0706544715 7
 
< 0.1%
0.01174191053 7
 
< 0.1%
0.004047489382 6
 
< 0.1%
0.0006895384412 6
 
< 0.1%
0.008826170864 5
 
< 0.1%
Other values (15727) 15786
73.6%
ValueCountFrequency (%)
0 5604
26.1%
6.936731185 × 10-81
 
< 0.1%
7.1451732 × 10-81
 
< 0.1%
6.552407722 × 10-71
 
< 0.1%
3.0667509 × 10-61
 
< 0.1%
3.784418754 × 10-61
 
< 0.1%
4.888453635 × 10-61
 
< 0.1%
6.035934568 × 10-61
 
< 0.1%
6.275761731 × 10-61
 
< 0.1%
7.012680094 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.6850103306 1
< 0.1%
0.6422459624 1
< 0.1%
0.6271506289 1
< 0.1%
0.6219419702 1
< 0.1%
0.615808966 1
< 0.1%
0.6021406588 1
< 0.1%
0.5914613398 1
< 0.1%
0.5878884741 1
< 0.1%
0.5862848412 1
< 0.1%
0.5828351037 1
< 0.1%

ghsl_built_1990_to_2000
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17102
Distinct (%)79.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.012688907
Minimum0
Maximum0.5155339
Zeros4188
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size167.7 KiB
2023-01-14T21:36:03.237967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.2799332 × 10-5
median0.0010009187
Q30.0081277131
95-th percentile0.071948676
Maximum0.5155339
Range0.5155339
Interquartile range (IQR)0.0080849138

Descriptive statistics

Standard deviation0.032745104
Coefficient of variation (CV)2.5806087
Kurtosis46.047888
Mean0.012688907
Median Absolute Deviation (MAD)0.0010009187
Skewness5.5442282
Sum272.22781
Variance0.0010722418
MonotonicityNot monotonic
2023-01-14T21:36:03.488330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4188
 
19.5%
0.07261158427 45
 
0.2%
0.001264274582 10
 
< 0.1%
0.002113715264 9
 
< 0.1%
0.005888817369 7
 
< 0.1%
0.0003145534636 7
 
< 0.1%
0.02489618726 7
 
< 0.1%
0.01355264961 7
 
< 0.1%
0.005869507069 7
 
< 0.1%
0.0001304532186 6
 
< 0.1%
Other values (17092) 17161
80.0%
ValueCountFrequency (%)
0 4188
19.5%
7.36501113 × 10-81
 
< 0.1%
6.508978702 × 10-71
 
< 0.1%
8.168245713 × 10-71
 
< 0.1%
2.544445235 × 10-61
 
< 0.1%
2.72110266 × 10-61
 
< 0.1%
2.800744662 × 10-61
 
< 0.1%
3.476675529 × 10-61
 
< 0.1%
3.597221066 × 10-61
 
< 0.1%
3.682270706 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.5155338956 1
< 0.1%
0.4792300467 1
< 0.1%
0.4776524267 1
< 0.1%
0.476911915 1
< 0.1%
0.4736488252 1
< 0.1%
0.4693984317 1
< 0.1%
0.4538649902 1
< 0.1%
0.4489680074 1
< 0.1%
0.4218545143 1
< 0.1%
0.42127213 1
< 0.1%

ghsl_built_2000_to_2014
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18053
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.018386307
Minimum0
Maximum0.64915892
Zeros3237
Zeros (%)15.1%
Negative0
Negative (%)0.0%
Memory size167.7 KiB
2023-01-14T21:36:03.739175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.00012405033
median0.0018705597
Q30.014936327
95-th percentile0.099875982
Maximum0.64915892
Range0.64915892
Interquartile range (IQR)0.014812277

Descriptive statistics

Standard deviation0.040420834
Coefficient of variation (CV)2.1984205
Kurtosis23.287708
Mean0.018386307
Median Absolute Deviation (MAD)0.0018705597
Skewness3.9858008
Sum394.45984
Variance0.0016338439
MonotonicityNot monotonic
2023-01-14T21:36:04.004012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3237
 
15.1%
0.01293090129 45
 
0.2%
0.0009296136635 10
 
< 0.1%
0.004802212748 9
 
< 0.1%
0.01253215712 7
 
< 0.1%
0.00345887835 7
 
< 0.1%
0.008034651972 7
 
< 0.1%
0.06937701573 7
 
< 0.1%
0.0005550943475 7
 
< 0.1%
0.008168905512 6
 
< 0.1%
Other values (18043) 18112
84.4%
ValueCountFrequency (%)
0 3237
15.1%
7.972580123 × 10-71
 
< 0.1%
8.065205574 × 10-71
 
< 0.1%
9.570129652 × 10-71
 
< 0.1%
1.041454036 × 10-61
 
< 0.1%
1.10308117 × 10-61
 
< 0.1%
1.167224284 × 10-61
 
< 0.1%
2.125206995 × 10-61
 
< 0.1%
2.914012605 × 10-61
 
< 0.1%
3.581201389 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.6491589154 1
< 0.1%
0.579944202 1
< 0.1%
0.5657850979 1
< 0.1%
0.4905589025 1
< 0.1%
0.4739498395 1
< 0.1%
0.4478762264 1
< 0.1%
0.4249122131 1
< 0.1%
0.4195887538 1
< 0.1%
0.4118305246 1
< 0.1%
0.4101385512 1
< 0.1%

ghsl_not_built_up
Real number (ℝ)

Distinct18966
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.87379989
Minimum0.00085933956
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size167.7 KiB
2023-01-14T21:36:04.287347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.00085933956
5-th percentile0.23814703
Q10.89786738
median0.99191887
Q30.99953243
95-th percentile1
Maximum1
Range0.99914066
Interquartile range (IQR)0.10166505

Descriptive statistics

Standard deviation0.23929401
Coefficient of variation (CV)0.27385447
Kurtosis3.4994257
Mean0.87379989
Median Absolute Deviation (MAD)0.0080811321
Skewness-2.1348925
Sum18746.503
Variance0.057261622
MonotonicityNot monotonic
2023-01-14T21:36:04.544484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2325
 
10.8%
0.5220279159 45
 
0.2%
0.9760159675 10
 
< 0.1%
0.9673857443 9
 
< 0.1%
0.9255443156 7
 
< 0.1%
0.9987787924 7
 
< 0.1%
0.8371330075 7
 
< 0.1%
0.9742944783 7
 
< 0.1%
0.8432828709 7
 
< 0.1%
0.9958814055 6
 
< 0.1%
Other values (18956) 19024
88.7%
ValueCountFrequency (%)
0.0008593395634 1
< 0.1%
0.0009833758159 1
< 0.1%
0.004179615187 1
< 0.1%
0.004741980312 1
< 0.1%
0.006977793015 1
< 0.1%
0.008067237962 1
< 0.1%
0.00907890323 1
< 0.1%
0.009630134503 1
< 0.1%
0.01026416457 1
< 0.1%
0.01028977285 1
< 0.1%
ValueCountFrequency (%)
1 2325
10.8%
0.9999991832 1
 
< 0.1%
0.9999974344 1
 
< 0.1%
0.9999972789 1
 
< 0.1%
0.999997086 1
 
< 0.1%
0.9999965233 1
 
< 0.1%
0.9999963884 1
 
< 0.1%
0.9999917968 1
 
< 0.1%
0.9999916958 1
 
< 0.1%
0.99999155 1
 
< 0.1%

ghsl_pop_density
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19882
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.756775
Minimum0
Maximum1741.2565
Zeros1397
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size167.7 KiB
2023-01-14T21:36:04.787343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.8485329
median17.632672
Q363.225683
95-th percentile538.75621
Maximum1741.2565
Range1741.2565
Interquartile range (IQR)59.37715

Descriptive statistics

Standard deviation209.70476
Coefficient of variation (CV)2.1899731
Kurtosis14.880748
Mean95.756775
Median Absolute Deviation (MAD)16.60438
Skewness3.6055606
Sum2054365.9
Variance43976.087
MonotonicityNot monotonic
2023-01-14T21:36:05.029197image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1397
 
6.5%
358.9100574 45
 
0.2%
44.39664085 10
 
< 0.1%
38.39799515 9
 
< 0.1%
35.61097599 7
 
< 0.1%
60.98269611 7
 
< 0.1%
12.46856118 7
 
< 0.1%
40.53305086 7
 
< 0.1%
20.40109574 7
 
< 0.1%
62.13962484 6
 
< 0.1%
Other values (19872) 19952
93.0%
ValueCountFrequency (%)
0 1397
6.5%
1.489816944 × 10-61
 
< 0.1%
3.391646709 × 10-51
 
< 0.1%
8.230325855 × 10-51
 
< 0.1%
0.0001108301438 1
 
< 0.1%
0.0002315190853 1
 
< 0.1%
0.0002631584597 1
 
< 0.1%
0.0002787421901 1
 
< 0.1%
0.0002962045376 1
 
< 0.1%
0.0003179559628 1
 
< 0.1%
ValueCountFrequency (%)
1741.256516 1
< 0.1%
1715.37757 1
< 0.1%
1715.048843 1
< 0.1%
1686.805022 1
< 0.1%
1679.134223 1
< 0.1%
1663.160424 1
< 0.1%
1660.9532 1
< 0.1%
1657.17349 1
< 0.1%
1654.763644 1
< 0.1%
1653.060676 1
< 0.1%

landcover_crops_fraction
Real number (ℝ)

Distinct21269
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.034265
Minimum0
Maximum80.064918
Zeros22
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size167.7 KiB
2023-01-14T21:36:05.292039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.52060433
Q15.6105152
median18.509291
Q333.590293
95-th percentile50.794426
Maximum80.064918
Range80.064918
Interquartile range (IQR)27.979777

Descriptive statistics

Standard deviation16.751106
Coefficient of variation (CV)0.79637228
Kurtosis-0.55590836
Mean21.034265
Median Absolute Deviation (MAD)13.873646
Skewness0.56286531
Sum451269.12
Variance280.59954
MonotonicityNot monotonic
2023-01-14T21:36:05.534899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.992261138 45
 
0.2%
0 22
 
0.1%
8.164316512 10
 
< 0.1%
15.63145216 9
 
< 0.1%
14.60886168 7
 
< 0.1%
11.78726281 7
 
< 0.1%
17.93840588 7
 
< 0.1%
12.0862425 7
 
< 0.1%
9.648788869 7
 
< 0.1%
15.77386212 6
 
< 0.1%
Other values (21259) 21327
99.4%
ValueCountFrequency (%)
0 22
0.1%
1.803807838 × 10-51
 
< 0.1%
7.026828431 × 10-51
 
< 0.1%
0.0002561146108 1
 
< 0.1%
0.0003822104756 1
 
< 0.1%
0.0005095612677 1
 
< 0.1%
0.0006392236666 1
 
< 0.1%
0.000848185808 1
 
< 0.1%
0.001028633319 1
 
< 0.1%
0.001283244848 1
 
< 0.1%
ValueCountFrequency (%)
80.06491789 1
< 0.1%
78.8611158 1
< 0.1%
78.62770717 1
< 0.1%
78.21836795 1
< 0.1%
77.90964149 1
< 0.1%
77.85394336 1
< 0.1%
77.24169375 1
< 0.1%
77.21041891 1
< 0.1%
77.15980606 1
< 0.1%
76.9228726 1
< 0.1%

landcover_urban_fraction
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20877
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.999061
Minimum0
Maximum98.784092
Zeros414
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size167.7 KiB
2023-01-14T21:36:05.798742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.098912126
Q10.79884119
median2.7701588
Q312.621501
95-th percentile76.911166
Maximum98.784092
Range98.784092
Interquartile range (IQR)11.82266

Descriptive statistics

Standard deviation23.715485
Coefficient of variation (CV)1.6940768
Kurtosis2.8804093
Mean13.999061
Median Absolute Deviation (MAD)2.4384455
Skewness2.0161259
Sum300335.85
Variance562.42422
MonotonicityNot monotonic
2023-01-14T21:36:06.035601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 414
 
1.9%
44.43827187 45
 
0.2%
9.450420981 10
 
< 0.1%
7.756364112 9
 
< 0.1%
14.35960965 7
 
< 0.1%
8.24759524 7
 
< 0.1%
4.30216981 7
 
< 0.1%
3.704300716 7
 
< 0.1%
14.78645024 7
 
< 0.1%
12.49155024 6
 
< 0.1%
Other values (20867) 20935
97.6%
ValueCountFrequency (%)
0 414
1.9%
0.0001240487166 1
 
< 0.1%
0.0001244831508 1
 
< 0.1%
0.0001246059642 1
 
< 0.1%
0.0001285736543 1
 
< 0.1%
0.0002113726117 1
 
< 0.1%
0.0002454157776 1
 
< 0.1%
0.0002481424591 1
 
< 0.1%
0.0002557726893 1
 
< 0.1%
0.0002570582655 1
 
< 0.1%
ValueCountFrequency (%)
98.78409191 1
< 0.1%
98.30161765 1
< 0.1%
98.23373433 1
< 0.1%
98.13041264 1
< 0.1%
98.01080365 1
< 0.1%
97.9019848 1
< 0.1%
97.84496702 1
< 0.1%
97.74975058 1
< 0.1%
97.69284394 1
< 0.1%
97.67493179 1
< 0.1%

landcover_water_permanent_10km_fraction
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11194
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4868488
Minimum0
Maximum99.164018
Zeros10142
Zeros (%)47.3%
Negative0
Negative (%)0.0%
Memory size167.7 KiB
2023-01-14T21:36:06.302649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.00051557883
Q30.15236441
95-th percentile5.5058273
Maximum99.164018
Range99.164018
Interquartile range (IQR)0.15236441

Descriptive statistics

Standard deviation6.5501709
Coefficient of variation (CV)4.4054049
Kurtosis72.195363
Mean1.4868488
Median Absolute Deviation (MAD)0.00051557883
Skewness7.6537522
Sum31898.854
Variance42.904739
MonotonicityNot monotonic
2023-01-14T21:36:06.552498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10142
47.3%
4.86909423 45
 
0.2%
0.08171667119 10
 
< 0.1%
0.37697643 9
 
< 0.1%
0.2425321891 7
 
< 0.1%
5.265976786 6
 
< 0.1%
7.249501695 5
 
< 0.1%
0.7517154626 5
 
< 0.1%
0.9135959239 5
 
< 0.1%
0.2932024689 4
 
< 0.1%
Other values (11184) 11216
52.3%
ValueCountFrequency (%)
0 10142
47.3%
6.533112427 × 10-71
 
< 0.1%
1.13067713 × 10-61
 
< 0.1%
1.755016715 × 10-61
 
< 0.1%
2.20794635 × 10-61
 
< 0.1%
6.186031026 × 10-61
 
< 0.1%
6.623356457 × 10-61
 
< 0.1%
7.76700294 × 10-61
 
< 0.1%
9.82939076 × 10-61
 
< 0.1%
1.043837964 × 10-51
 
< 0.1%
ValueCountFrequency (%)
99.16401816 1
< 0.1%
98.7889409 1
< 0.1%
98.78893956 1
< 0.1%
98.78892806 1
< 0.1%
98.78892716 1
< 0.1%
98.7889191 1
< 0.1%
98.78890103 1
< 0.1%
92.38671042 1
< 0.1%
91.78107995 1
< 0.1%
90.97455512 1
< 0.1%

landcover_water_seasonal_10km_fraction
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17702
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71489078
Minimum0
Maximum56.201637
Zeros3588
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size167.7 KiB
2023-01-14T21:36:06.790362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0013248167
median0.029151075
Q30.38196895
95-th percentile3.2756275
Maximum56.201637
Range56.201637
Interquartile range (IQR)0.38064414

Descriptive statistics

Standard deviation2.3837011
Coefficient of variation (CV)3.3343571
Kurtosis95.791979
Mean0.71489078
Median Absolute Deviation (MAD)0.029151075
Skewness8.1477972
Sum15337.267
Variance5.6820311
MonotonicityNot monotonic
2023-01-14T21:36:07.027693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3588
 
16.7%
1.719901986 45
 
0.2%
0.4528529494 10
 
< 0.1%
0.6447054008 9
 
< 0.1%
0.0004785929443 7
 
< 0.1%
0.00317501242 7
 
< 0.1%
0.0005754871078 7
 
< 0.1%
0.4626489201 7
 
< 0.1%
1.864077522 6
 
< 0.1%
0.04655988518 6
 
< 0.1%
Other values (17692) 17762
82.8%
ValueCountFrequency (%)
0 3588
16.7%
3.39203139 × 10-61
 
< 0.1%
4.4158927 × 10-61
 
< 0.1%
1.133675166 × 10-51
 
< 0.1%
1.324671291 × 10-51
 
< 0.1%
1.335088398 × 10-51
 
< 0.1%
1.427589648 × 10-51
 
< 0.1%
1.429606073 × 10-51
 
< 0.1%
1.588313242 × 10-51
 
< 0.1%
2.040639461 × 10-51
 
< 0.1%
ValueCountFrequency (%)
56.20163691 1
< 0.1%
50.56816886 1
< 0.1%
43.20166455 1
< 0.1%
42.9797931 1
< 0.1%
39.9685785 1
< 0.1%
39.80161585 1
< 0.1%
39.13537858 1
< 0.1%
38.58140046 1
< 0.1%
38.48168031 1
< 0.1%
35.91742564 1
< 0.1%

nighttime_lights
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11253
Distinct (%)52.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5065428
Minimum0
Maximum382.93277
Zeros10074
Zeros (%)47.0%
Negative0
Negative (%)0.0%
Memory size167.7 KiB
2023-01-14T21:36:07.299919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.13733271
Q34.8301943
95-th percentile55.486404
Maximum382.93277
Range382.93277
Interquartile range (IQR)4.8301943

Descriptive statistics

Standard deviation21.090378
Coefficient of variation (CV)2.4793125
Kurtosis17.426181
Mean8.5065428
Median Absolute Deviation (MAD)0.13733271
Skewness3.7000205
Sum182499.37
Variance444.80403
MonotonicityNot monotonic
2023-01-14T21:36:07.547772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10074
47.0%
12.69709496 45
 
0.2%
0.6864953137 10
 
< 0.1%
1.58128617 9
 
< 0.1%
1.22848272 7
 
< 0.1%
1.311014238 7
 
< 0.1%
2.148200208 7
 
< 0.1%
0.9671025484 6
 
< 0.1%
1.565415046 6
 
< 0.1%
0.225086966 5
 
< 0.1%
Other values (11243) 11278
52.6%
ValueCountFrequency (%)
0 10074
47.0%
0.0001496930743 1
 
< 0.1%
0.0001787458648 1
 
< 0.1%
0.0001796777242 1
 
< 0.1%
0.0002116325581 1
 
< 0.1%
0.0002837618983 1
 
< 0.1%
0.0003353672012 1
 
< 0.1%
0.0003633572429 1
 
< 0.1%
0.0003639023291 1
 
< 0.1%
0.0003682120868 1
 
< 0.1%
ValueCountFrequency (%)
382.9327688 1
< 0.1%
167.4461583 1
< 0.1%
154.2381551 1
< 0.1%
151.9722349 1
< 0.1%
151.7742639 1
< 0.1%
150.8818834 1
< 0.1%
149.9927437 1
< 0.1%
149.2415112 1
< 0.1%
148.8221119 1
< 0.1%
148.4191856 1
< 0.1%

dist_to_capital
Real number (ℝ)

Distinct21290
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean289.72227
Minimum0.10530635
Maximum1897.3516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size167.7 KiB
2023-01-14T21:36:07.805126image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.10530635
5-th percentile8.2815965
Q1115.89087
median256.73647
Q3401.15307
95-th percentile647.93474
Maximum1897.3516
Range1897.2463
Interquartile range (IQR)285.2622

Descriptive statistics

Standard deviation238.81178
Coefficient of variation (CV)0.8242783
Kurtosis7.0597198
Mean289.72227
Median Absolute Deviation (MAD)142.58929
Skewness1.9713099
Sum6215701.5
Variance57031.065
MonotonicityNot monotonic
2023-01-14T21:36:08.593354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.46935671 45
 
0.2%
278.6301227 10
 
< 0.1%
261.5968335 9
 
< 0.1%
307.5219805 7
 
< 0.1%
369.8153578 7
 
< 0.1%
430.633407 7
 
< 0.1%
303.8056591 7
 
< 0.1%
83.00545759 7
 
< 0.1%
10.21794893 6
 
< 0.1%
393.6626358 6
 
< 0.1%
Other values (21280) 21343
99.5%
ValueCountFrequency (%)
0.1053063522 1
< 0.1%
0.243460189 1
< 0.1%
0.2723535385 1
< 0.1%
0.2883147586 1
< 0.1%
0.3186927104 1
< 0.1%
0.3484557676 1
< 0.1%
0.3836564044 1
< 0.1%
0.5072512792 1
< 0.1%
0.5114668429 1
< 0.1%
0.5503411931 1
< 0.1%
ValueCountFrequency (%)
1897.351575 1
< 0.1%
1881.50215 1
< 0.1%
1814.235364 1
< 0.1%
1813.948149 1
< 0.1%
1812.226403 1
< 0.1%
1810.390771 1
< 0.1%
1801.34182 1
< 0.1%
1799.527368 1
< 0.1%
1794.243679 1
< 0.1%
1778.057321 1
< 0.1%

dist_to_shoreline
Real number (ℝ)

Distinct21290
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean402.60854
Minimum0.11208033
Maximum1769.5239
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size167.7 KiB
2023-01-14T21:36:08.838203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.11208033
5-th percentile7.5339698
Q1126.37946
median327.27197
Q3643.91067
95-th percentile1042.5241
Maximum1769.5239
Range1769.4118
Interquartile range (IQR)517.53121

Descriptive statistics

Standard deviation321.51728
Coefficient of variation (CV)0.79858536
Kurtosis-0.484433
Mean402.60854
Median Absolute Deviation (MAD)252.72005
Skewness0.6249039
Sum8637563.6
Variance103373.36
MonotonicityNot monotonic
2023-01-14T21:36:09.063071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
969.5296642 45
 
0.2%
1223.327336 10
 
< 0.1%
895.4534605 9
 
< 0.1%
660.5835943 7
 
< 0.1%
682.5466964 7
 
< 0.1%
729.6785061 7
 
< 0.1%
685.1395234 7
 
< 0.1%
899.9594426 7
 
< 0.1%
963.1664218 6
 
< 0.1%
820.5107717 6
 
< 0.1%
Other values (21280) 21343
99.5%
ValueCountFrequency (%)
0.1120803302 1
< 0.1%
0.4368265905 1
< 0.1%
0.4505270134 1
< 0.1%
0.457539723 1
< 0.1%
0.467026867 1
< 0.1%
0.5283029991 1
< 0.1%
0.5425026754 1
< 0.1%
0.5455659434 1
< 0.1%
0.5518094693 1
< 0.1%
0.5635888988 1
< 0.1%
ValueCountFrequency (%)
1769.523906 2
< 0.1%
1699.132541 3
< 0.1%
1595.368011 2
< 0.1%
1564.151157 1
 
< 0.1%
1542.589582 1
 
< 0.1%
1499.002204 1
 
< 0.1%
1479.130147 1
 
< 0.1%
1468.991174 2
< 0.1%
1451.861364 2
< 0.1%
1446.970356 1
 
< 0.1%

Target
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21454
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35073609
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size167.7 KiB
2023-01-14T21:36:09.331911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.11045163
Q10.19577166
median0.2935741
Q30.49900277
95-th percentile0.70749965
Maximum1
Range1
Interquartile range (IQR)0.3032311

Descriptive statistics

Standard deviation0.19437554
Coefficient of variation (CV)0.55419315
Kurtosis-0.51996401
Mean0.35073609
Median Absolute Deviation (MAD)0.12577741
Skewness0.66561048
Sum7524.6921
Variance0.037781851
MonotonicityNot monotonic
2023-01-14T21:36:09.592256image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1327826553 1
 
< 0.1%
0.4509127794 1
 
< 0.1%
0.1252256788 1
 
< 0.1%
0.4742699233 1
 
< 0.1%
0.5386413516 1
 
< 0.1%
0.571884876 1
 
< 0.1%
0.2501356095 1
 
< 0.1%
0.3385579499 1
 
< 0.1%
0.5288058609 1
 
< 0.1%
0.2460801998 1
 
< 0.1%
Other values (21444) 21444
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.0003220136428 1
< 0.1%
0.0006669851474 1
< 0.1%
0.003018080409 1
< 0.1%
0.004575079538 1
< 0.1%
0.00489837095 1
< 0.1%
0.00609375181 1
< 0.1%
0.006285133496 1
< 0.1%
0.006563564294 1
< 0.1%
0.007080796214 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.993478219 1
< 0.1%
0.9888278568 1
< 0.1%
0.9804689238 1
< 0.1%
0.9797684348 1
< 0.1%
0.9787069806 1
< 0.1%
0.9765504151 1
< 0.1%
0.9747127435 1
< 0.1%
0.9714261221 1
< 0.1%
0.9643150612 1
< 0.1%

Interactions

2023-01-14T21:35:54.380142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:23.601589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:29.060375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:32.663297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:10.945510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:14.453253image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:17.817184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:21.474952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:25.348284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:28.796236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:32.353500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:35.712875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:39.812290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:43.310282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:46.846138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:50.699939image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:55.122851image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:24.829277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:29.324594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:32.889750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:11.148386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:14.661126image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:18.018066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:21.664841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:25.550160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:29.010105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:32.544590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:35.924748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:40.006027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:43.506666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:47.042017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:50.905327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:55.317731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:25.192086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:29.534464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:33.084141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:11.385142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:14.867999image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:18.241926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:21.874712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:25.762035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:29.220976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:32.741787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:36.133622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:40.223892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:43.715529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:47.251887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:51.114203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:55.507614image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:25.421731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:29.764279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:33.276022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:11.592015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:15.067876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:18.442803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:22.066593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:25.974904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:29.429846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:32.941177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:36.327016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:40.420776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:43.914406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:47.456762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:51.321721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:55.768463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:25.715352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:30.015703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:33.502871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:11.817877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:15.279746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:18.668543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:22.277465image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:26.199769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:29.661704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:33.169036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:36.555879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:40.637901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:44.127883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:47.684622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:51.543583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:55.979332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:26.039488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:30.223134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:08.632383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:12.036741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:15.480620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:19.058140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:22.473343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:26.409652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:29.892075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:33.377916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:36.773253image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:40.854768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:44.353742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:47.908994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:51.751456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:56.204206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:26.459326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:30.429306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:08.852624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:12.248613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:15.700485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:19.278370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:22.704301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:26.626525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:30.116615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:33.601289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:36.986122image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:41.069635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:44.615189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:48.146341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:51.973318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:56.414073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:26.884719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:30.628183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:09.058496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:12.453485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:15.902362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:19.510227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:22.903615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:26.825412image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:30.325898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:33.794170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:37.207990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:41.300498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:44.839050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:48.351790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:52.179196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:56.638939image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:27.174488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:30.845049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:09.284513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:12.684345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:16.118229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:19.733093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:23.122484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:27.036285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:30.584738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:34.001552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:37.463339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:41.524364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:45.061914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:48.563660image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:52.397075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:56.889785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:27.516293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:31.082843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:09.499381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:12.918199image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:16.344090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:19.960949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:23.340502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:27.268652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:30.818453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:34.219419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:37.724432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:41.752223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:45.297769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:48.784524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:52.618937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:57.089170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:27.753407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:31.312946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:09.695260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:13.128070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:16.542967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:20.162760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:24.106050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:27.476526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:31.027326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:34.436636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:37.934705image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:41.973086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:45.508940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:48.991401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:52.828809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:57.292043image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:28.000233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:31.531899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:09.890141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:13.342937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:16.748842image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:20.382624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:24.317919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:27.701388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:31.249188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:34.643508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:38.134582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:42.187954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:45.728805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:49.207283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:53.060666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:57.508921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:28.199575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:31.801530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:10.101010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:13.564801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:16.960710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:20.596493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:24.511799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:27.917254image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:31.458305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:34.851389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:38.350449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:42.399029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:45.944681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:49.432145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:53.466925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:57.751283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:28.428870image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:32.031387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:10.315878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:13.801655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:17.180576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:20.826352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:24.725669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:28.145119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:31.685876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:35.061270image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:38.572313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:42.648390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:46.185533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:49.699987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:53.705294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:57.973157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:28.658623image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:32.259247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:10.542757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:14.022519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:17.391446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:21.045217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:24.930541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:28.358992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:31.911761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:35.278140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:39.372552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:42.877251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:46.408403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:50.142719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:53.948406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:58.195025image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:28.873491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:34:32.465120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:10.757625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:14.243382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:17.623301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:21.275075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:25.142411image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:28.588855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:32.135630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:35.503004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:39.599420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:43.097315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:46.629271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:50.428837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-14T21:35:54.170269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-01-14T21:36:09.861494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
yearghsl_water_surfaceghsl_built_pre_1975ghsl_built_1975_to_1990ghsl_built_1990_to_2000ghsl_built_2000_to_2014ghsl_not_built_upghsl_pop_densitylandcover_crops_fractionlandcover_urban_fractionlandcover_water_permanent_10km_fractionlandcover_water_seasonal_10km_fractionnighttime_lightsdist_to_capitaldist_to_shorelineTargetcountryurban_or_rural
year1.000-0.055-0.078-0.043-0.047-0.0070.0590.0070.130-0.049-0.007-0.042-0.055-0.0380.0850.1270.5400.059
ghsl_water_surface-0.0551.0000.3070.2600.2880.223-0.5070.266-0.2280.2880.6700.5910.288-0.071-0.2420.2400.0810.178
ghsl_built_pre_1975-0.0780.3071.0000.8100.8610.766-0.8580.734-0.3750.7570.3100.2910.718-0.250-0.3440.5880.1410.455
ghsl_built_1975_to_1990-0.0430.2600.8101.0000.8500.790-0.8320.732-0.2420.7450.2910.2760.695-0.261-0.2490.6090.1120.434
ghsl_built_1990_to_2000-0.0470.2880.8610.8501.0000.878-0.9010.778-0.2580.7850.3320.3020.720-0.283-0.2700.6030.0790.337
ghsl_built_2000_to_2014-0.0070.2230.7660.7900.8781.000-0.8480.746-0.1830.7250.2800.2510.654-0.285-0.1680.5360.0670.331
ghsl_not_built_up0.059-0.507-0.858-0.832-0.901-0.8481.000-0.7800.323-0.783-0.477-0.423-0.7240.2620.301-0.6070.1220.555
ghsl_pop_density0.0070.2660.7340.7320.7780.746-0.7801.000-0.1200.8530.3330.2680.774-0.250-0.1210.6250.1180.531
landcover_crops_fraction0.130-0.228-0.375-0.242-0.258-0.1830.323-0.1201.000-0.173-0.124-0.138-0.2560.0150.458-0.2170.2150.244
landcover_urban_fraction-0.0490.2880.7570.7450.7850.725-0.7830.853-0.1731.0000.3220.2870.826-0.240-0.2110.7040.1120.682
landcover_water_permanent_10km_fraction-0.0070.6700.3100.2910.3320.280-0.4770.333-0.1240.3221.0000.7800.338-0.175-0.1630.2650.0770.026
landcover_water_seasonal_10km_fraction-0.0420.5910.2910.2760.3020.251-0.4230.268-0.1380.2870.7801.0000.311-0.074-0.1790.2590.0860.023
nighttime_lights-0.0550.2880.7180.6950.7200.654-0.7240.774-0.2560.8260.3380.3111.000-0.220-0.3180.7340.1060.370
dist_to_capital-0.038-0.071-0.250-0.261-0.283-0.2850.262-0.2500.015-0.240-0.175-0.074-0.2201.0000.066-0.2550.3120.100
dist_to_shoreline0.085-0.242-0.344-0.249-0.270-0.1680.301-0.1210.458-0.211-0.163-0.179-0.3180.0661.000-0.3340.4080.182
Target0.1270.2400.5880.6090.6030.536-0.6070.625-0.2170.7040.2650.2590.734-0.255-0.3341.0000.2170.688
country0.5400.0810.1410.1120.0790.0670.1220.1180.2150.1120.0770.0860.1060.3120.4080.2171.0000.185
urban_or_rural0.0590.1780.4550.4340.3370.3310.5550.5310.2440.6820.0260.0230.3700.1000.1820.6880.1851.000

Missing values

2023-01-14T21:35:58.568973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-14T21:35:59.246670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDcountryyearurban_or_ruralghsl_water_surfaceghsl_built_pre_1975ghsl_built_1975_to_1990ghsl_built_1990_to_2000ghsl_built_2000_to_2014ghsl_not_built_upghsl_pop_densitylandcover_crops_fractionlandcover_urban_fractionlandcover_water_permanent_10km_fractionlandcover_water_seasonal_10km_fractionnighttime_lightsdist_to_capitaldist_to_shorelineTarget
0ID_AAIethGyEthiopia2016R0.00.0000000.0000000.0000550.0005360.99940812.14613425.4896590.8794840.0000000.0000000.000000278.788451769.3383780.132783
1ID_AAYiaCeLEthiopia2005R0.00.0000000.0001100.0000000.0000180.999872113.80671664.1360530.6014270.0000000.0054270.000000200.986978337.1352430.004898
2ID_AAdurmKjMozambique2009R0.00.0000000.0000000.0000000.0000001.0000000.0000004.4000960.1319000.0000000.0030780.000000642.594208169.9137730.097320
3ID_AAgNHlesMalawi2015R0.00.0001410.0001810.0002540.0002280.9991955.21332025.3793712.01713611.2938410.1310350.000000365.349451613.5916100.304107
4ID_AAishfNDGuinea2012U0.00.0116490.0175600.0173830.0998750.85353331.7346615.08162022.8159840.0050470.1304751.461894222.867189192.9263630.605328
5ID_AAnetgMrEthiopia2016U0.00.0086230.0194090.0598860.0826820.829400203.58050724.62943331.2357080.0000000.00822322.9819709.803702487.7908520.463882
6ID_ABOoaqlICameroon2004R0.00.0001310.0001490.0001680.0000930.9994580.3429500.0117980.2513950.0000000.0002890.000000290.621111477.5633530.133302
7ID_ABRVEEtGGhana2014R0.00.0012980.0010420.0002190.0003470.99709414.34086042.5945764.1809470.0115110.1124400.000000438.103890435.8162710.545138
8ID_ABbCEbbLSenegal2010U0.00.0000000.0007510.0014850.0000350.99772919.46819911.3327162.1912060.4103654.7468940.528963482.262914320.8673280.590187
9ID_ABcnWkmCEthiopia2016R0.00.0000000.0000000.0000000.0000001.0000000.0000001.1050110.1471960.0000000.2301720.000000533.1391371016.5803040.122272
IDcountryyearurban_or_ruralghsl_water_surfaceghsl_built_pre_1975ghsl_built_1975_to_1990ghsl_built_1990_to_2000ghsl_built_2000_to_2014ghsl_not_built_upghsl_pop_densitylandcover_crops_fractionlandcover_urban_fractionlandcover_water_permanent_10km_fractionlandcover_water_seasonal_10km_fractionnighttime_lightsdist_to_capitaldist_to_shorelineTarget
21444ID_zyoBSDvFTanzania2008U0.00.0000000.0007900.0003490.0006430.99821910.48811233.1688055.8505902.1399800.2155560.000000257.499432456.4321930.340661
21445ID_zytjzoBKCote d'Ivoire2011U0.00.2320910.0000000.0369420.0213230.709643262.4308316.45567725.2066111.0472010.80502734.589127207.73192816.8047680.584942
21446ID_zyyHDTQbKenya2014U0.00.0000750.0002440.0003190.0001880.99917420.14102249.0900950.4832740.0005780.0059770.000000140.722881277.3617400.273127
21447ID_zyzHAKILMalawi2015R0.00.0056280.0027400.0096020.0367900.94524014.39829633.0623336.3022110.0000000.0000000.00000065.359441502.4744870.241307
21448ID_zzJKKqnwNigeria2013U0.00.0129320.0431550.0524990.2150500.67636489.52096615.35539421.5334640.9639100.96202718.633171474.79351012.0905520.649643
21449ID_zzPvDPjQNigeria2013R0.00.0029610.0082400.0023130.0080680.97841844.04435212.5519786.3029020.0000000.0000001.955632283.861037159.7900570.624088
21450ID_zzQoPhjZSenegal2011R0.00.0000000.0000000.0000000.0000001.0000000.00000027.8485710.3722670.0000000.0000000.000000295.307249122.9769600.250042
21451ID_zzQuWZBFGhana2014R0.00.0005360.0000920.0000180.0000740.9992790.4581433.6799341.7121360.0000000.0000000.442422166.405249155.3653550.314871
21452ID_zzqCGLvbGhana2014R0.00.0000000.0000000.0000000.0000001.0000000.00000012.3317630.9601630.0018990.0611260.000000568.759665534.6386280.237831
21453ID_zzqQlHgYMozambique2011R0.00.0000350.0000000.0020730.0003370.99755615.22365515.1920341.5653080.0000000.0000000.0000001486.151015216.5194080.165071